{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d4f2509a",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset,DataLoader\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b13f6968",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#加载数据集\n",
    "class DiabetDataset(Dataset):\n",
    "    def __init__(self,filepath):\n",
    "        xy = np.loadtxt(filepath,delimiter = ',', dtype = np.float32)\n",
    "        self.len = xy.shape[0]\n",
    "        self.x_data = torch.from_numpy(xy[:,:-1])\n",
    "        self.y_data = torch.from_numpy(xy[:,[-1]])\n",
    "    def __getitem__(self,index):\n",
    "        return self.x_data[index], self.y_data[index]\n",
    "    def __len__(self):\n",
    "        return self.len\n",
    "    \n",
    "dataset = DiabetDataset(r\"D:\\BaiduNetdiskDownload\\PyTorch深度学习实践\\diabetes.csv.gz\")\n",
    "train_load = DataLoader(dataset = dataset,\n",
    "                       batch_size = 32,\n",
    "                       shuffle = True,\n",
    "                       num_workers = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9a90f769",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#构造模型\n",
    "class model(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(model, self).__init__()\n",
    "        self.linear1 = torch.nn.Linear(8,6)\n",
    "        self.linear2 = torch.nn.Linear(6,4)\n",
    "        self.linear3 = torch.nn.Linear(4,1)\n",
    "        self.sigmoid = torch.nn.Sigmoid()\n",
    "    def forward(self,x):\n",
    "        x = self.sigmoid(self.linear1(x))\n",
    "        x = self.sigmoid(self.linear2(x))\n",
    "        x = self.sigmoid(self.linear3(x))\n",
    "        return x\n",
    "Model = model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4da93540",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#构造损失函数和优化器\n",
    "criterion = torch.nn.BCELoss(size_average = True)\n",
    "optimizer = torch.optim.SGD(Model.parameters(),lr = 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b89ea104",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "99 5 0.6834421157836914\n",
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      "99 7 0.6439642906188965\n",
      "99 8 0.7826741337776184\n",
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      "99 10 0.6832964420318604\n",
      "99 11 0.6444423198699951\n",
      "99 12 0.6440162062644958\n",
      "99 13 0.6044418811798096\n",
      "99 14 0.5649471282958984\n",
      "99 15 0.762810230255127\n",
      "99 16 0.6834360957145691\n",
      "99 17 0.5851969122886658\n",
      "99 18 0.6241220831871033\n",
      "99 19 0.5849584937095642\n",
      "99 20 0.6636801362037659\n",
      "99 21 0.6239020228385925\n",
      "99 22 0.6044327616691589\n",
      "99 23 0.6742748022079468\n"
     ]
    }
   ],
   "source": [
    "#训练模型\n",
    "if __name__ == '__main__':\n",
    "    for epoch in range(100):\n",
    "        for i,data in enumerate(train_load,0):\n",
    "            inputs,labels = data\n",
    "            y_pred = Model(inputs)\n",
    "            loss = criterion(y_pred, labels)\n",
    "            print(epoch,i,loss.item())\n",
    "            #backward\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffe190f9",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
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